Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data

Autor: Jonas E. Böhler, Michael E. Schaepman, Mathias Kneubühler

Publikováno v: Remote Sensing, Vol 12, Iss 8, p 1256 (2020)

Crop species separation is essential for a wide range of agricultural applications—in particular, when seasonal information is needed. In general, remote sensing can provide such information with high accuracy, but in small structured agricultural

Externí odkaz: https://doaj.org/article/cc516c50d9a64ba0a54666c89fb29cc0

BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES

Autor: ŞİMŞEK, Murat, ILGIN, Hakki Alparslan

Publikováno v: Volume: 62, Issue: 1 53-62
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

Due to the high spectral resolution, hyperspectral images need large data storage and processing time. Indeed, its high dimensional structure requires high computational complexity, especially for target detection. In order to overcome these problems

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=tubitakulakb::64e04032c0aec3afed775d9c4a7076c9
https://dergipark.org.tr/tr/pub/aupse/issue/51879/676083

Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction

Autor: Adil Khan, Mohammed A. Alqarni, Rasheed Hussain, Muhammad Ahmad, Manuel Mazzara, Salvatore Distefano

Publikováno v: Ahmad, M, Alqarni, M A, Khan, A M, Hussain, R, Mazzara, M & Distefano, S 2019, ' Segmented and non-segmented stacked denoising autoencoder for hyperspectral band reduction ', Optik, vol. 180, pp. 370-378 . https://doi.org/10.1016/j.ijleo.2018.10.142

Hyperspectral image analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information. Existing band reduction (BR) methods have the capability to reveal the nonlinear properties exhi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f86011d36ef75ea6c671b5fc19020edd
http://arxiv.org/abs/1705.06920

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